Graduate employability remains a critical concern for higher education institutions, policymakers, and employers alike. This study explores the key determinants influencing the employability of graduates from Universiti Sains Islam Malaysia (USIM). Using a mixed-methods approach that combines quantitative survey data and qualitative interviews with graduates, employers, and academic staff, the research identifies factors such as communication skills, work experience, academic performance, soft skills, and alignment of curriculum with industry needs as significant contributors to employability outcomes. The study also examines the role of Islamic values and character development as distinctive attributes promoted by USIM. Findings suggest that while technical and academic competencies are essential, holistic graduate attributes and industry engagement play a pivotal role in enhancing employability. Recommendations are offered to strengthen institutional strategies aimed at bridging the gap between education and employment in the Malaysian context.

The article examines the problem of smoothing seasonal fluctuations in inbound tourism through the design and implementation of specialized off-season programs and itineraries. The relevance of the study derives from the fact that the temporal concentration of tourist flows intensifies the phenomenon of overtourism, undermining the economic resilience, social well-being, and ecological balance of destinations. This paper attempts a critical analysis of desezonalization strategies in the cases of Cyprus, Malta, and Georgia by extracting some universal factors that guarantee success. The novelty introduced here is by making a comparison between mature and developing destinations through their market structure, stakeholder coordination capacity prism, and an application of big-data analytics to air accessibility management. In methodology, it undertakes a systematic literature review. It processes official statistics on tourist arrivals for the years 2017 and 2023, in addition to comparing national strategic documents on product diversification, market development, transport infrastructure, and institutional governance. The main findings are: (1) without a paradigmatic shift from quantitative growth to higher revenue per tourist and quality of experience, formal diversification of supply does not work, (2) in mature destinations, political will and an embedded mechanism of inter-agency coordination decide, as evidenced by Malta that reduced the seasonality coefficient to 1.53, (3) developing markets can rapidly curb seasonality when they depend on data-based expansion of air networks and targeting market with reverse seasonality as demonstrated by Georgia, and finally (4) key barrier still lies within the implementation gap manifested through business model inertia and insufficient incentive for year-round operation. On this basis, a cyclical model of seasonality management is proposed, integrating long-term strategic planning, big-data analytics, and public–private partnership. The article will be helpful to researchers of sustainable tourism, designers of national and regional strategies, and destination marketing practitioners seeking to reduce seasonal dependence.

This article presents a comprehensive methodology for quantitatively assessing the effectiveness of sales channels in global marketing projects, based on currency-normalized data, media-mix modeling, and the integration of predictive customer lifetime value. The goal of the study is to create a unified analytical framework that precisely measures marginal return on investment across countries with different tax and currency environments, identifies the causal contribution of media channels, and dynamically optimizes the budget. The work gets its timeliness from the fact that, at present, there is significant volatility in personal identifiers and currency; hence, aggregated, privacy-preserving measurement becomes an imperative need for international brands. FX-adjusted media-mix modeling weekly granularity with Bayesian layers, algorithmic attribution through Markov chains with exponential decay, and incremental geo-holdout plus user experiments for causal verification, followed by real-time bid management on early PLTV forecasts— that is the novelty in approach when all four independent parts of synergy onto a single automated data platform. The fact is that translating both costs and revenue in one currency eliminates up to 85% region-to-region variance in ROI. Compared to the use of deterministic rules, media-mix regression with saturation functions and carryover effects improves the accuracy of marginal forecasts by about 20–30%. Algorithmic attribution fixes the built-in overvaluation of last touch by redistributing the budget to where in the funnel it is found, and which upper-funnel formats are undervalued. It also keeps coefficients fresh as the market shifts, with constant cycles of testing and learning. Composite channel rating—that means elasticity, incremental lift, and PLTV signals all joined—enables auto reallocation with a bump up in marginal ROI while holding the target margin at the country and format level. Researchers, data analysts, and practicing marketers who happen to be fiddling with international media budgets and trying to optimize that pesky sales funnel will find this article a goldmine.